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Clean up README with full bug analysis for ZAI

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biondizzle
2026-04-09 06:21:04 +00:00
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README.md
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# vLLM GLM Tool Parser Patch # vLLM GLM-5.x Tool Calling Patches
Patches vLLM's GLM-4/GLM-5.1 tool parser to fix multiple issues with tool call handling. Fixes two critical bugs that prevent GLM models from working correctly with OpenAI-compatible tool calling in vLLM.
## Issues Fixed ## Summary
### Issue 1: Tool Response Content Ignored (CRITICAL) GLM-5.x models would either crash or silently drop tool response content when using the OpenAI chat completions API with tools. Two separate bugs were responsible:
**Symptom:** When the model makes a tool call and receives a response, it would act as if the response was empty ("The function returned no output") even though valid content was provided. 1. **Tool parser regex mismatch** — Parser expected newline between function name and arguments, but GLM's template does not include one
2. **Content format detection failure** — vLLM auto-detected "openai" format incorrectly, causing tool response content to be dropped
**Root Cause:** Two bugs working together: ---
1. **Tool parser regex mismatch** (`glm4_moe_tool_parser.py`): The `func_detail_regex` required a newline between the function name and first argument tag, but GLM-5.1's chat template doesn't include that newline. The regex silently failed to match. ## Bug #1: Tool Parser Regex Mismatch
2. **Content format detection wrong** (`vllm/renderers/hf.py`): vLLM detected "openai" content format because the GLM template has `{% for tr in m.content %}` for tool responses. But the template then checks `m.content is string` which is False for OpenAI format arrays, causing content to be dropped. ### Problem
**Model output format (no newline after name):** The `func_detail_regex` in `glm4_moe_tool_parser.py` required a literal newline between the function name and the first argument tag.
```
[TOOL_CALL_START]function_name[ARG_KEY]value[ARG_END]...[TOOL_CALL_END] GLM-5.x chat template outputs tool calls without that newline - the function name is immediately followed by the first argument tag. The regex would fail to match, causing tool call extraction to fail silently.
```
### Fix
Changed the regex to use `\\s*` (optional whitespace) instead of mandatory `\\n`, and made the arguments group optional for zero-argument calls:
**Old regex (broken):**
```python ```python
r"\[TOOL_CALL_START\]([^\n]*)\n(.*)\[TOOL_CALL_END\]" # Requires \n after name # Before
r"\[TOOL_START\]([^\n]*)\n(.*)\[TOOL_END\]"
# After
r"\[TOOL_START\]\s*([\w.\-]+)\s*((?:\[ARG_KEY\].*)?)\s*\[TOOL_END\]"
``` ```
**Fixed regex:** Also fixed `tc_args_raw` to default to empty string, preventing crashes on zero-argument tool calls.
**File:** `glm4_moe_tool_parser.py`
---
## Bug #2: Content Format Detection Failure
### Problem
vLLM's `_detect_content_format()` function analyzes Jinja templates to determine whether message content should be formatted as strings or OpenAI-style arrays.
For GLM-5.x, the template contains a loop `{% for tr in m.content %}` for handling tool responses with multiple results. vLLM saw this loop and detected "openai" format, converting tool message content to:
```json
[{"type": "text", "text": "the actual content"}]
```
However, the GLM template's first branch checks `{% if m.content is string %}` before using that loop. Since arrays are not strings, the template took the wrong branch and the content was lost.
The model would respond: *"The function returned no output"* even though valid content was provided.
### Root Cause
The template has two branches for tool messages:
```jinja
{%- if m.content is string %}
{{ '<observations>' + m.content + '</observations>' }}
{%- else %}
{% for tr in m.content %} <!-- expects objects with .name property -->
...
{% endif %}
```
vLLM's detection saw the `for` loop and chose "openai" format. But the `is string` check failed for arrays, and the `else` branch expected objects with `.name` properties that `{"type": "text"}` objects don't have.
### Fix
Added `_is_glm_model()` detection function to `vllm/renderers/hf.py` that forces "string" content format for GLM models, bypassing the incorrect auto-detection:
```python ```python
r"\[TOOL_CALL_START\]\s*([\w.\-]+)\s*((?:\[ARG_KEY\].*)?)\s*\[TOOL_CALL_END\]" def _is_glm_model(tokenizer: HfTokenizer, model_config: "ModelConfig") -> bool:
"""Check if this is a GLM model that requires string content format."""
name_or_path = tokenizer.name_or_path.lower()
glm_indicators = ["glm-4", "glm-5", "glm4", "glm5", "zai-org/glm"]
return any(ind in name_or_path for ind in glm_indicators)
``` ```
**Content format fix:** Called in `_resolve_chat_template_content_format()` before auto-detection.
Added `_is_glm_model()` detection to force "string" content format for GLM models, bypassing the incorrect auto-detection.
### Issue 2: Zero-Argument Tool Calls Crash **File:** `vllm_patches/hf.py`
**Symptom:** `TypeError: 'NoneType' object is not iterable` when tool has no arguments. ---
**Fix:** The `tc_args_raw` is now defaulted to empty string: `tc_args_raw = tc_detail.group(2) or ""`
### Issue 3: Streaming Path vs Non-Streaming Path Inconsistency
Both paths now use the same robust extraction helpers for consistency.
## Files ## Files
@@ -49,57 +93,41 @@ Both paths now use the same robust extraction helpers for consistency.
| `glm4_moe_tool_parser.py` | Fixed tool parser (regex fix) | | `glm4_moe_tool_parser.py` | Fixed tool parser (regex fix) |
| `utils.py` | Utility functions for partial JSON/tag handling | | `utils.py` | Utility functions for partial JSON/tag handling |
| `vllm_patches/hf.py` | Patched renderer (content format fix) | | `vllm_patches/hf.py` | Patched renderer (content format fix) |
| `Dockerfile` | Overlays patched files onto base image | | `Dockerfile` | Overlays patched files onto base vLLM image |
| `Jenkinsfile` | CI/CD pipeline for building and pushing |
| `tests/` | Test suite for tool call validation |
## Testing ---
### Requirements
```bash
pip install httpx regex
```
### Running Tests
```bash
export VLLM_API_BASE="https://api.vultrinference.com/v1"
export VLLM_API_KEY="your-api-key"
export VLLM_MODEL="zai-org/GLM-5.1-FP8"
python tests/test_tool_diagnosis.py
```
### Test Cases
| Test | Description |
|------|-------------|
| `test_simple_tool_response` | Verifies model can see tool response content |
| `test_without_tools_param` | Tests behavior without tools param in follow-up |
| `test_different_content_formats` | String vs array content formats |
## Deployment ## Deployment
### Jenkins Pipeline ### Docker Build
```bash ```bash
curl -X POST "https://jenkins.sweetapi.com/job/vllm-glm-build/buildWithParameters" \ docker build -t your-registry/vllm-glm51-patched:latest .
-u "admin:TOKEN" \ docker push your-registry/vllm-glm51-patched:latest
-d "IMAGE_TAG=latest"
``` ```
### Manual Build ### Kubernetes
```bash Update your deployment to use the patched image and ensure these vLLM args:
docker build -t atl.vultrcr.com/vllm/vllm-glm51-patched:latest .
docker push atl.vultrcr.com/vllm/vllm-glm51-patched:latest ```yaml
extraArgs:
- "--tool-call-parser=glm47"
- "--enable-auto-tool-choice"
``` ```
### Images ---
- Base: `vllm/vllm-openai:glm51-cu130` ## Verification
- Output: `atl.vultrcr.com/vllm/vllm-glm51-patched:<tag>`
Tool response content is now properly passed to the model:
```
Model response: The test function was called successfully! It returned the value **42**.
PASS: Model referenced the tool result (42)
```
---
## Related ## Related

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tests/test_tool_debug.py
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#!/usr/bin/env python3
"""
Debug test to see what prompt the model actually receives.
"""
import httpx
import json
API_BASE = "https://api.vultrinference.com/v1"
API_KEY = "26DN7PNUB3YRBEPCDNMXKKD6ZODMETRSMOZQ"
MODEL = "zai-org/GLM-5.1-FP8"
def test_with_echo():
"""
Test with echo=True to see the prompt tokens.
"""
messages = [
{"role": "user", "content": "Call the test function"},
{
"role": "assistant",
"tool_calls": [{
"id": "call_123",
"type": "function",
"function": {"name": "test_func", "arguments": "{}"}
}]
},
{
"role": "tool",
"tool_call_id": "call_123",
"content": "VALUE_42"
}
]
tools = [{
"type": "function",
"function": {
"name": "test_func",
"description": "A test function",
"parameters": {"type": "object", "properties": {}}
}
}]
with httpx.Client(timeout=60.0) as client:
# Try to get prompt logprobs which might show us the prompt
response = client.post(
f"{API_BASE}/chat/completions",
headers={
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
},
json={
"model": MODEL,
"messages": messages,
"tools": tools,
"stream": False,
"max_tokens": 100,
"logprobs": True,
"top_logprobs": 1,
"echo": True # Return prompt tokens
}
)
result = response.json()
print("Full response:")
print(json.dumps(result, indent=2, ensure_ascii=False))
def test_tool_only_message():
"""
Test if a tool-only message (no tools param) works.
This is what worked in the previous test.
"""
messages = [
{"role": "user", "content": "What is 2+2?"},
{
"role": "assistant",
"tool_calls": [{
"id": "call_123",
"type": "function",
"function": {"name": "calc", "arguments": "{}"}
}],
"content": None
},
{
"role": "tool",
"tool_call_id": "call_123",
"content": "The answer is 42"
}
]
# NO tools param - this worked before
with httpx.Client(timeout=60.0) as client:
response = client.post(
f"{API_BASE}/chat/completions",
headers={
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
},
json={
"model": MODEL,
"messages": messages,
# NO tools param
"stream": False,
"max_tokens": 100
}
)
result = response.json()
if "choices" in result:
content = result["choices"][0]["message"]["content"]
print(f"\nNo tools param - Response: {content}")
print(f"Contains 42: {'42' in content}")
else:
print(f"\nNo tools param - Error: {result}")
def test_with_tools_param():
"""
Test WITH tools param - this is what fails.
"""
messages = [
{"role": "user", "content": "What is 2+2?"},
{
"role": "assistant",
"tool_calls": [{
"id": "call_123",
"type": "function",
"function": {"name": "calc", "arguments": "{}"}
}],
"content": None
},
{
"role": "tool",
"tool_call_id": "call_123",
"content": "The answer is 42"
}
]
tools = [{
"type": "function",
"function": {
"name": "calc",
"description": "Calculator",
"parameters": {"type": "object", "properties": {}}
}
}]
with httpx.Client(timeout=60.0) as client:
response = client.post(
f"{API_BASE}/chat/completions",
headers={
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
},
json={
"model": MODEL,
"messages": messages,
"tools": tools, # WITH tools param
"stream": False,
"max_tokens": 100
}
)
result = response.json()
content = result["choices"][0]["message"]["content"]
print(f"\nWith tools param - Response: {content}")
print(f"Contains 42: {'42' in content}")
def test_without_assistant_tool_calls():
"""
Test if the issue is the assistant message with tool_calls.
What if we just send user -> tool response?
"""
messages = [
{"role": "user", "content": "The calculator returned this result"},
{
"role": "tool",
"tool_call_id": "call_123",
"content": "VALUE_IS_42"
}
]
with httpx.Client(timeout=60.0) as client:
response = client.post(
f"{API_BASE}/chat/completions",
headers={
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
},
json={
"model": MODEL,
"messages": messages,
"stream": False,
"max_tokens": 100
}
)
result = response.json()
if "choices" in result:
content = result["choices"][0]["message"]["content"]
print(f"\nNo assistant tool_calls - Response: {content}")
print(f"Contains 42: {'42' in content}")
else:
print(f"\nError: {result}")
if __name__ == "__main__":
print("=" * 60)
print("Debugging tool response visibility")
print("=" * 60)
test_tool_only_message()
test_with_tools_param()
test_without_assistant_tool_calls()

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tests/test_tool_visibility.py
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#!/usr/bin/env python3
"""
Minimal test - is the tool response content being passed to the model?
"""
import httpx
import json
API_BASE = "https://api.vultrinference.com/v1"
API_KEY = "26DN7PNUB3YRBEPCDNMXKKD6ZODMETRSMOZQ"
MODEL = "zai-org/GLM-5.1-FP8"
def test_direct_prompt():
"""
If we could send a direct prompt, what would it look like?
GLM-5.1 expects tool responses in <observations> tags:
<observations>{"result": "42"}</observations>
Let's test if the model can see content in that format.
"""
# Simulate what the prompt SHOULD look like after chat template
messages = [
{"role": "user", "content": "What did the function return?"},
{
"role": "assistant",
"content": "I'll call the function.",
"tool_calls": [{
"id": "call_123",
"type": "function",
"function": {"name": "get_value", "arguments": "{}"}
}]
},
{
"role": "tool",
"tool_call_id": "call_123",
"content": "UNIQUE_MARKER_42"
}
]
tools = [{
"type": "function",
"function": {
"name": "get_value",
"description": "Get a value",
"parameters": {"type": "object", "properties": {}}
}
}]
with httpx.Client(timeout=60.0) as client:
response = client.post(
f"{API_BASE}/chat/completions",
headers={
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
},
json={
"model": MODEL,
"messages": messages,
"tools": tools,
"stream": False,
"max_tokens": 100
}
)
result = response.json()
if "choices" in result:
content = result["choices"][0]["message"]["content"]
print(f"Model response: {content}")
print(f"Contains UNIQUE_MARKER_42: {'UNIQUE_MARKER_42' in content}")
else:
print(f"Error: {result}")
def test_fake_tool_response_in_user_message():
"""
Test: What if we put the tool response in a user message instead?
This bypasses the role="tool" handling entirely.
"""
messages = [
{"role": "user", "content": "What did the function return?"},
{
"role": "assistant",
"content": "I called the function.",
"tool_calls": [{
"id": "call_123",
"type": "function",
"function": {"name": "get_value", "arguments": "{}"}
}]
},
# Instead of role="tool", use user message
{"role": "user", "content": "The function returned: UNIQUE_MARKER_42"}
]
tools = [{
"type": "function",
"function": {
"name": "get_value",
"description": "Get a value",
"parameters": {"type": "object", "properties": {}}
}
}]
with httpx.Client(timeout=60.0) as client:
response = client.post(
f"{API_BASE}/chat/completions",
headers={
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
},
json={
"model": MODEL,
"messages": messages,
"tools": tools,
"stream": False,
"max_tokens": 100
}
)
result = response.json()
if "choices" in result:
content = result["choices"][0]["message"]["content"]
print(f"\nUser message hack - Model response: {content}")
print(f"Contains UNIQUE_MARKER_42: {'UNIQUE_MARKER_42' in content}")
else:
print(f"Error: {result}")
def test_tool_response_as_observation_format():
"""
Test: What if we format the tool response in the GLM expected format?
GLM expects: <observations>content</observations>
"""
# Try putting the observations tag in the content
messages = [
{"role": "user", "content": "What did the function return?"},
{
"role": "assistant",
"content": "I called the function.",
"tool_calls": [{
"id": "call_123",
"type": "function",
"function": {"name": "get_value", "arguments": "{}"}
}]
},
{
"role": "tool",
"tool_call_id": "call_123",
"content": "<observations>UNIQUE_MARKER_42</observations>"
}
]
tools = [{
"type": "function",
"function": {
"name": "get_value",
"description": "Get a value",
"parameters": {"type": "object", "properties": {}}
}
}]
with httpx.Client(timeout=60.0) as client:
response = client.post(
f"{API_BASE}/chat/completions",
headers={
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
},
json={
"model": MODEL,
"messages": messages,
"tools": tools,
"stream": False,
"max_tokens": 100
}
)
result = response.json()
if "choices" in result:
content = result["choices"][0]["message"]["content"]
print(f"\nWith <observations> tags - Model response: {content}")
print(f"Contains UNIQUE_MARKER_42: {'UNIQUE_MARKER_42' in content}")
else:
print(f"Error: {result}")
if __name__ == "__main__":
print("Testing tool response visibility")
print("=" * 60)
test_direct_prompt()
test_fake_tool_response_in_user_message()
test_tool_response_as_observation_format()